Vast amounts of information is encoded in tables found in
documents, on the Web, and in spreadsheets or databases. Integrating or
searching over this information benefits from understanding its intended
meaning and making it explicit in a semantic representation language
like RDF. Most current approaches to generating Semantic Web representations
from tables requires human input to create schemas and
often results in graphs that do not follow best practices for linked data.
Evidence for a table’s meaning can be found in its column headers, cell
values, implicit relations between columns, caption and surrounding text
but also requires general and domain-specific background knowledge. We
describe techniques grounded in graphical models and probabilistic reasoning
to infer meaning associated with a table. Using background knowledge
from the Linked Open Data cloud, we jointly infer the semantics
of column headers, table cell values (e.g., strings and numbers) and relations
between columns and represent the inferred meaning as graph of
RDF triples. A table’s meaning is thus captured by mapping columns to
classes in an appropriate ontology, linking cell values to literal constants,
implied measurements, or entities in the linked data cloud (existing or
new) and discovering or and identifying relations between columns.